Erzsebet Eva Borbas
@wisc.edu
Scopus Publications
Scopus Publications
Nicholas R. Nalli, Cheng Dang, James A. Jung, Robert O. Knuteson, E. Eva Borbas, Benjamin T. Johnson, Ken Pryor, and Lihang Zhou
MDPI AG
Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.
Yufen Ma, Wei Han, Zhenglong Li, E. Eva Borbas, Ali Mamtimin, and Yongqiang Liu
MDPI AG
Infrared (IR) land surface emissivity (LSE) plays an important role in numerical weather prediction (NWP) models through the satellite radiance assimilation. However, due to the large uncertainties in LSE over the desert, many land-surface sensitive channels of satellite IR sensors are not assimilated. This calls for further assessments of the quality of satellite-retrieved LSE in these desert regions. A set of LSE observations were made from field experiments conducted on 16–18 October 2013 along a south/north desert road in the Taklimakan Desert (TD), China. The observed LSEs (EOBS) are thus used in this study as the reference values to evaluate the quality of Combined ASTER MODIS Emissivity over Land (CAMEL) data. Analysis of these data shows four main results. First, the CAMEL datasets appear to sufficiently capture the spatial variations in LSE from the oasis to the hinterland of the TD (this is especially the case in the quartz reststrahlen band). From site 1 at the southern edge of the Taklimakan Desert to site 10 at the northern edge, the measured LSE and the corresponding CAMEL observation in the quartz reststrahlen band first decrease and reach their minimum around sites 4–6 in the hinterland of the Taklimakan Desert. Then, the LSE increases gradually and finally reaches its maximum at site 10, which has a clay ground surface, showing that the LSE is higher at the edges of the desert and lower in the center. Second, the CAMEL values at 11.3 μm have a zonal distribution characterized by a northeast–southwest strike, though such an artifact might have been introduced by ASTER LSE data during the merging process that created the CAMEL dataset. Third, the unrealistic variation of the original EOBS can be filtered out with useful signals, as identified by the first six principal components of the PCA conducted on the laboratory-measured hyperspectral emissivity spectra (ELAB). Fourth, the CAMEL results correlate well with the measured LSE at the 10 observation sites, with the observed LSE being slightly smaller than the CAMEL values in general.
Michael J. Foster, Coda Phillips, Andrew K. Heidinger, Eva E. Borbas, Yue Li, W. Paul Menzel, Andi Walther, and Elisabeth Weisz
American Meteorological Society
Abstract A new version of the PATMOS-x multidecadal cloud record, version 6.0, has been produced and is available from the NOAA National Centers for Environmental Information. A description of the processes and methods used for generating the dataset are presented, with a focus on the differences between version 6.0 and the previous version of PATMOS-x, version 5.3. The new version appears both to be more stable, with less intersatellite variability, and to have more consistent polar cloud detection, phase distribution, and cloud-top height distribution when compared against the MODIS EOS record. Improvements in consistency and performance are attributed to the addition of multidimensional variables for cloud detection, constraining cloud retrievals to radiometric bands available throughout the record, and the addition of data from the HIRS instrument. Significance Statement The PATMOS-x project produces multidecadal cloudiness records from polar-orbiting satellites. Version 6.0 combines imager and sounder data from 15 satellites and shows significant improvements in accuracy and stability.
E. Eva Borbas, Elisabeth Weisz, Chris Moeller, W. Paul Menzel, and Bryan A. Baum
Copernicus GmbH
Abstract. An operational data product available for both the Suomi National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) platforms provides high-spatial-resolution infrared (IR) absorption band radiances for Visible Infrared Imaging Radiometer Suite (VIIRS) based on a VIIRS and Crosstrack Infrared Sounder (CrIS) data fusion method. This study investigates the use of these IR radiances, centered at 4.5, 6.7, 7.3, 9.7, 13.3, 13.6, 13.9, and 14.2 µm, to construct atmospheric moisture products (e.g., total precipitable water and upper tropospheric humidity) and to evaluate their accuracy. Total precipitable water (TPW) and upper tropospheric humidity (UTH) retrieved from hyperspectral sounder CrIS measurements are provided at the associated VIIRS sensor's high spatial resolution (750 m) and are compared subsequently to collocated operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and S-NPP VIIRS moisture products. This study suggests that the use of VIIRS IR absorption band radiances will provide continuity with Aqua MODIS moisture products.
Eva E. Borbas and Paul W. Menzel
MDPI AG
This paper compares the tropospheric moisture data records derived from High-resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements from the years 2003 through 2013. Total Precipitable Water Vapor (TPW) and Upper Tropospheric Precipitable Water Vapor (UTPW) are derived using the infrared spectral bands in the CO2 and H2O absorption bands as well as in the atmospheric windows. Retrieval of TPW and UTPW uses a statistical regression algorithm performed using clear sky radiances (and Brightness Temperatures) measured over land and ocean for both day and night. The TPW and UTPW seasonal cycles of HIRS and MODIS observations are found to be in synchronization with zonal mean values for one degree latitude bands within 2.0 mm and 0.07 mm, respectively.
Wen Chen, Rachel T. Pinker, Yingtao Ma, Glynn Hulley, Eva Borbas, Tanvir Islam, Kerry-A. Cawse-Nicholson, Simon Hook, Chris Hain, and Jeff Basara
American Meteorological Society
ABSTRACTLand surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).
Michelle Loveless, E. Eva Borbas, Robert Knuteson, Kerry Cawse-Nicholson, Glynn Hulley, and Simon Hook
MDPI AG
The Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) Version 2 (V002) has been available since March 2019 from the NASA LP DAAC (Land Processes Distributed Active Archive Center) and provides global, monthly infrared land surface emissivity and uncertainty at 0.05 degrees (~5 km) resolution. A climatology of the CAMEL V002 product is now available at the same spatial, temporal, and spectral resolution, covering the CAMEL record from 2000 to 2016. Characterization of the climatology over case sites and IGBP (International Geosphere-Biosphere Programme) land cover categories shows the climatology is a stable representation of the monthly CAMEL emissivity. Time series of the monthly CAMEL V002 product show realistic seasonal changes but also reveal subtle artifacts known to be from calibration and processing errors in the MODIS MxD11 emissivity. The use of the CAMEL V002 climatology mitigates many of these time dependent errors by providing an emissivity estimate which represents the complete 16-year record. The CAMEL V002 climatology’s integration into RTTOV (Radiative Transfer for TOVS) v12 is demonstrated through the simulation of IASI (Infrared Atmospheric Sounding Interferometer) radiances. Improved stability in CAMEL Version 3 is expected in the future with the incorporation of the new MxD21 and VIIRS VNP21 emissivity products in MODIS Collection 6.1.
Yunheng Xue, Jun Li, W. Paul Menzel, Eva Borbas, Shu‐Peng Ho, Zhenglong Li, and Jinlong Li
American Geophysical Union (AGU)
This study quantifies the characteristics of different satellite sampling errors in the time series of total precipitable water (TPW) derived from Constellation System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation, Special Sensor Microwave Imager Sounder (SSMIS), and High‐resolution Infrared Radiation Sounder (HIRS) during the overlapping time period of January 2007 to December 2013. Gap‐free data from ERA5 reanalysis of the European Centre for Medium Range Weather Forecasts are used as reference values. All TPW data are first compared with microwave radiometer measurements from Atmospheric Radiation Measurement Program. In general, they are consistent, with all their regression coefficients being greater than 0.77. Discrepancies in global TPW time series can be mainly attributed to the inherent sampling errors of these three different satellite remote sensing systems. COSMIC has small sampling errors in higher latitudes. But it has scarce samples in tropical regions, which leads to a large sampling error of 3.00 mm in the estimation of global TPW. Sampling in SSMIS is more uniform with mean errors less than 0.5 mm. But the sampling is only over the ocean. Sampling errors in HIRS are larger in tropics and north subtropical areas due to clear sky biased sampling. Moreover, it is significantly correlated with the variability of TPW, whereas the sampling error in COSMIC is less influenced by TPW. Sampling errors will be reduced and more consistent global TPW time series will be derived by simply combining the multisensor samplings together.
Rachel T. Pinker, Yingtao Ma, Wen Chen, Glynn Hulley, Eva Borbas, Tanvir Islam, Chris Hain, Kerry Cawse-Nicholson, Simon Hook, and Jeff Basara
MDPI AG
Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites.
Caroline A. Famiglietti, Joshua B. Fisher, Gregory Halverson, and Eva E. Borbas
American Geophysical Union (AGU)
This analysis is the first global validation of the Moderate Resolution Imaging Spectroradiometer (MODIS)‐derived near‐surface air temperature and dew point estimates, which both serve as crucial input data in models of energy, water, and carbon exchange between terrestrial ecosystems and the atmosphere. By hypsometrically interpolating the MOD07 Level‐2 atmospheric profile product to surface pressure level, we obtained near‐surface air temperature and dew point observations at 5 km pixel resolution. We compared these daily data, retrieved over a 14‐year record, to corresponding measurements from 109 ground meteorological stations (FLUXNET). Our results show strong agreement between satellite and in situ near‐surface air temperature measurements (R2 = 0.89, root‐mean‐square error = 3.47°C, and bias = −0.19°C) and dew point observations (R2 = 0.76, root‐mean‐square error = 5.04°C, and bias = 0.79°C) with insignificant differences in error across climate zones. This validation is among the earliest assessments of the reprocessed, crosstalk‐corrected Collection 6.1 Terra MODIS data and provides support for widespread applications of near‐surface atmospheric data.
Michelle Feltz, Eva Borbas, Robert Knuteson, Glynn Hulley, and Simon Hook
MDPI AG
Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 μm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 μm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 μm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m2.
Marc Schröder, Maarit Lockhoff, Frank Fell, John Forsythe, Tim Trent, Ralf Bennartz, Eva Borbas, Michael G. Bosilovich, Elisa Castelli, Hans Hersbach,et al.
Copernicus GmbH
Abstract. The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude–latitude grid of 2∘ × 2∘. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988–2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes.
Michelle Feltz, Eva Borbas, Robert Knuteson, Glynn Hulley, and Simon Hook
MDPI AG
Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types.
E. Borbas, Glynn Hulley, Michelle Feltz, Robert Knuteson, and Simon Hook
MDPI AG
As part of a National Aeronautics and Space Administration (NASA) MEaSUREs (Making Earth System Data Records for Use in Research Environments) Land Surface Temperature and Emissivity project, the Space Science and Engineering Center (UW-Madison) and the NASA Jet Propulsion Laboratory (JPL) developed a global monthly mean emissivity Earth System Data Record (ESDR). This new Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UW BF) and the JPL ASTER Global Emissivity Dataset Version 4 (GEDv4). The dataset includes monthly global records of emissivity and related uncertainties at 13 hinge points between 3.6–14.3 µm, as well as principal component analysis (PCA) coefficients at 5-km resolution for the years 2000 through 2016. A high spectral resolution (HSR) algorithm is provided for HSR applications. This paper describes the 13 hinge-points combination methodology and the high spectral resolutions algorithm, as well as reports the current status of the dataset.
Christopher C. Moeller, Richard A. Frey, Eva Borbas, W. Paul Menzel, Truman Wilson, Aisheng Wu, and Xu Geng
SPIE
Observations in the Terra MODIS PVLWIR bands 27 – 30 are known to be influenced by electronic crosstalk from those bands as senders and into those same bands as receivers. The magnitude of this crosstalk affecting L1B radiances has been steadily increasing throughout the mission lifetime, and has resulted in several detectors within these bands to be unusable for making L2 and L3 science products. In recent years, the crosstalk contamination has been recognized as compromising the climate quality status of several MODIS L2 and L3 science products that depend on the PVLWIR bands. In response, the MODIS Characterization Support Team (MCST) has undertaken an effort to generate a crosstalk correction algorithm in the operational L1B radiance algorithm. The correction algorithm has been tested and established and crosstalk corrected L1B radiances have been tested in several Terra MODIS L2 science product algorithms, including MOD35 (Cloud Mask), MOD06 (Cloud Fraction, Cloud Particle Phase, Cloud Top Properties), and MOD07 (Water Vapor Profiles). Comparisons of Terra MODIS to Aqua MODIS and Terra MODIS to MetOp-A IASI show that long-term trends in Collection 6 L1B radiances and the associated L2 and L3 science products are greatly improved by the crosstalk correction. The crosstalk correction is slated for implementation into Collect 6.1 of MODIS processing.
W. Paul Menzel, Richard A. Frey, Eva E. Borbas, Bryan A. Baum, Geoff Cureton, and Nick Bearson
American Meteorological Society
AbstractThis paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (εf, or cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.
Jérôme Vidot and Éva Borbás
Wiley
This study describes the scientific approach and the validation of the visible and near‐infrared snow‐free land surface Bidirectional Reflectance Distribution Function (BRDF) atlas for Version 11 of the Radiative Transfer for the Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) (RTTOV) Forward Model. The atlas provides a global (at a spatial resolution of 0.1°) and monthly mean land surface BRDF for any instrument containing channels with a central wavelength between 0.4 and 2.5 µm, as well as a quality index of the BRDF. It is based on the reconstructed hyperspectral BRDF from the seven channels of the operational and global Moderate Resolution Imaging Spectroradiometer (MODIS) 16 days BRDF kernel‐driven product MCD43C1: a principal component analysis regression method was applied between the seven channels of the MODIS BRDF products and a set of the US Geological Survey hyperspectral reflectance measurements for soils, rocks, and mixtures of both and vegetation surfaces. The comparison of the RTTOV BRDF atlas against the Spinning Enhanced Visible and Infrared Imager (SEVIRI) surface black‐sky albedo products of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Land Satellite Application Facility on Land Surface Analysis (Land‐SAF) shows good spatial and temporal consistency of the RTTOV BRDF atlas when applied on three SEVIRI visible and near‐infrared channels. The RTTOV narrowband black‐sky albedo is retrieved within ±0.01 in absolute accuracy at 0.6 and 1.6 µm and is overestimated by something between 0.01 and 0.03 at 0.8 µm. The temporal variation of the RTTOV broadband black‐sky albedo is consistent with the EUMETSAT Land‐SAF SEVIRI products but overestimated by somewhere between 0.01 and 0.02 when considering the best quality index of the RTTOV BRDF atlas. Less agreement is found in two cases: (i) for extreme geometrical conditions when the satellite zenith angle is greater than 65° and (ii) for lower quality indices of the RTTOV BRDF atlas.
Guido Masiello, Carmine Serio, Sara Venafra, Italia DeFeis, and Eva E. Borbas
American Geophysical Union (AGU)
The problem of diurnal variation in surface emissivity over the Sahara Desert during non‐raining days is studied and assessed with observations from the Infrared Atmospheric Sounding Interferometer (IASI). The analysis has been performed over a Sahara Desert dune target area during July 2010. Spinning Enhanced Visible and Infrared Imager observations from the European geostationary platform Meteosat‐9 (Meteorological Satellite 9) have been also used to characterize the target area. Although the amplitude of this daily cycle has been shown to be very small, we argue that suitable nighttime meteorological conditions and the strong contrast of the reststrahlen absorption bands of quartz (8–14 μm) can amplify its effect over the surface spectral emissivity. The retrieval of atmospheric parameters show that, at nighttime, an atmospheric temperature inversion occurs close to the surface yielding a thin boundary layer which acts like a lid, keeping normal convective overturning of the atmosphere from penetrating through the inversion. This mechanism traps water vapor close to the land and drives the direct adsorption of water vapor at the surface during the night. The diurnal variation in emissivity at 8.7 μm has been found to be as large as 0.03 with high values at night and low values during the day. At 10.8 μm and 12 μm, the variation has the same sign as that at 8.7 μm, but with a smaller amplitude, 0.019 and 0.014, respectively. The impact of these diurnal variations on the retrieval of surface temperature and atmospheric parameters has been analyzed.
William L. Smith, Elisabeth Weisz, Stanislav V. Kireev, Daniel K. Zhou, Zhenglong Li, and Eva E. Borbas
American Meteorological Society
AbstractA fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression “clear trained” and “cloud trained” retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retrieval system. The improvement results from the fact that the nonlinear dependence of spectral radiance on the atmospheric variables, which is due to cloud altitude and associated atmospheric moisture concentration variations, is minimized as a result of the cloud-height-classification process. The detailed method and results from example applications of the DR retrieval algorithm are presented. The new DR method will be used to retrieve atmospheric profiles from Aqua AIRS, MetOp Infrared Atmospheric Sounding Interferometer, and the forthcoming Joint Polar Satellite System ultraspectral radiance data.
Elisabeth Weisz, W. Paul Menzel, Nadia Smith, Richard Frey, Eva E. Borbas, and Bryan A. Baum
American Meteorological Society
AbstractThe next-generation Visible and Infrared Imaging Radiometer Suite (VIIRS) offers infrared (IR)-window measurements with a horizontal spatial resolution of at least 1 km, but it lacks IR spectral bands that are sensitive to absorption by carbon dioxide (CO2) or water vapor (H2O). The CO2 and H2O absorption bands have high sensitivity for the inference of cloud-top pressure (CTP), especially for semitransparent ice clouds. To account for the lack of vertical resolution, the “merging gradient” (MG) approach is introduced, wherein the high spatial resolution of an imager is combined with the high vertical resolution of a sounder for improved CTP retrievals. The Cross-Track Infrared Sounder (CrIS) is on the same payload as VIIRS. In this paper Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data are used as proxies for VIIRS and CrIS, respectively, although the approach can be applied to any imager–sounder pair. The MG method establishes a regression relationship between gradients in both the sounder radiances convolved to imager bands and the sounder CTP retrievals. This relationship is then applied to the imager radiance measurements to obtain CTP retrievals at imager spatial resolution. Comparisons with Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud altitudes are presented for a variety of cloud scenes. Results demonstrate the ability of the MG algorithm to add spatial definition to the sounder retrievals with a higher accuracy and precision than those obtained solely from the imager.
Xingming Liang, Alexander Ignatov, Quanhua Liu, Yong Chen, David Groff, Xiaoxiong Xiong, Changyong Cao, Eva Borbas, and Simon Hook
SPIE
Monitoring of IR Clear-Sky Radiances over Oceans for SST nearreal time web-based system has been established in July 2008. It analyzes Model (Community Radiative Transfer Model, CRTM) minus Observation (M-O) biases in clear-sky ocean brightness temperatures (BT) in AVHRR bands 3.7 (IR37), 11 (IR11), and 12μm (IR12) onboard NOAA-16, -17, -18, -19 and Metop-A. In January 2012, AVHRR-like bands of VIIRS onboard the Suomi National Polar Partnership (S-NPP; launched in October 2012), and two MODIS instruments onboard Terra and Aqua, were included in MICROS. Double-differences are employed to check various sensors for radiometric consistency. The VIIRS and AVHRR have been in-family, and the consistency further improved after the VIIRS IR calibration was fine-tuned on 7 March 2012. However, MODIS M-O biases have been out of family (by -0.6K in IR 11, and -0.3K in IR12). Analyses have shown that these anomalies in MODIS M-O biases are caused by the "M" term, i.e., incorrect MODIS transmittance coefficients in CRTM v2.02. Based on feedback from NESDIS SST and U. Wisconsin Teams, CRTM Team updated transmittance coefficients in CRTM v2.10. As a result, MODIS M-O biases are now in agreement with AVHRR/VIIRS. However, cross-platform Terra/Aqua bias of ~0.3 K in Ch20 (3.9μm) remains, likely due to calibration uncertainties in MODIS L1b product. This paper documents the joint effort by the SST, MODIS Characterization Support and CRTM Teams towards identifying and resolving observed cross-platform inconsistencies.
Zhenglong Li, Jun Li, Xin Jin, Timothy J. Schmit, Eva E. Borbas, and Mitchell D. Goldberg
American Geophysical Union (AGU)
[1] Land surface emissivity (LSE) in the infrared (IR) window region (8–12 mm) governs the thermal emissions from the Earth’s surface. Many LSE databases, retrieved from various satellite instruments, are available for studying climate, Earth‐atmosphere interaction, weather, and the environment. The precision (standard deviation) and accuracy (bias) of these databases remain unclear. In this study, we introduce an objective and efficient method for quantitatively evaluating the LSE precision using satellite radiance observations. The LSE brightness temperature (Tb) deviations, defined as the standard deviations of Tb differences between satellite observations and radiative transfer calculations, can be estimated by minimizing the impacts from land surface temperature (LST) and atmospheric profiles. This is followed by the estimation of LSE precision. This method does not need the true LSE measurements. It only needs ancillary information such as atmospheric profiles and LST, both of which do not require high accuracy and thus can be obtained from a numerical weather prediction forecast or analysis. The method is applied to six different monthly LSE databases from August 2006 and 2007, and the results are presented. The error sources affecting the method are identified and the sensitivity to these errors is studied.
Elisabeth Weisz, Paul Menzel, Jun Li, Eva Borbas, and Robert Holz
OSA
Synergistic use of AIRS and MODIS measurements enables accurate cloud characterization and provides improved cloud property retrievals as shown in this paper with a focus on cloud top height.
Eva E. Borbas, W. Paul Menzel, Elisabeth Weisz, and Dezso Devenyi
American Meteorological Society
Abstract Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS–AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.
Youri Plokhenko, W. Paul Menzel, Henry E. Revercomb, Eva Borbas, Paolo Antonelli, and EliSabeth Weisz
Informa UK Limited
An approach using spatial analysis of satellite IR spectral measurements for quality assessment is presented. The second spatial differential is used as a model of measurement noise for spatially smooth radiative fields. Spatial differentiation significantly magnifies the noise contribution and reduces the physical signal amplitude because of differences in spatial distributions of instrument noise and atmospheric thermal fields. The second spatial differential represents a convenient and effective tool for numerical analysis of satellite IR measurements. This paper demonstrates that statistics of the second spatial differential are informative predictors for data‐quality characterization. Statistics of the second spatial differential are used for identifying anomalies in spectral channel data caused by detector noise, sensitivity loss to spatial shortwave thermal variations, and spatially (temporally) correlated noise.